ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2020
DOI: 10.1109/icassp40776.2020.9053610
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Multi-Microphone Complex Spectral Mapping for Speech Dereverberation

Abstract: This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) RI components of multiple microphones. We also investigate the integration of multi-microphone complex spectral mapping with beamforming and post-filtering. Experimental results on multi-channel speech dereverberatio… Show more

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Cited by 55 publications
(54 citation statements)
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References 39 publications
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“…With multi-channel complex spectral mapping, the explicit use of these inter-channel features does not produce performance gain, as shown in Table 3. This suggests that inter-channel features can be captured implicitly through a DNN that is trained for multi-channel complex spectral mapping, which is consistent with [16] for speech dereverberation.…”
Section: Resultssupporting
confidence: 65%
See 2 more Smart Citations
“…With multi-channel complex spectral mapping, the explicit use of these inter-channel features does not produce performance gain, as shown in Table 3. This suggests that inter-channel features can be captured implicitly through a DNN that is trained for multi-channel complex spectral mapping, which is consistent with [16] for speech dereverberation.…”
Section: Resultssupporting
confidence: 65%
“…Another useful spatial cue is the inter-channel phase difference (IPD) or inter-channel time difference (ITD), which is highly correlated with the direction of arrival with respect to the microphone array. Both IID and IPD (or ITD) can be implicitly exploited by performing multi-channel complex spectral mapping [16], where the IID and the IPD are encoded in the dual-channel complex spectrogram of the noisy mixture. In contrast to conventional beamforming that typically exploits second-order statistics of multiple channels, such an approach has the potential to extract all effective cues in dual-channel complex-domain inputs through deep learning.…”
Section: Dual-channel Complex Spectral Mappingmentioning
confidence: 99%
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“…In this context, a block-online approach is proposed in [13]- [15] to address continuous speaker separation, where speech signals from an unknown number of speakers, degraded by environmental noise, room reverberation and a wide range of speaker overlap, arrive as a continuous stream. These studies assume that in each fixed-length short processing block, typically 2.4-second long, there are at most two speakers talking, so that a two-speaker separation model based on for example utterance-wise PIT (uPIT) can be applied in each block for separation.…”
Section: Introductionmentioning
confidence: 99%
“…With fewer parameters, our best model outperforms the conformer in all scenarios for both utterance-wise and continuous evaluation. We should mention that a very recently posted paper [24] reports state-of-the-art results for the LibriCSS evaluation. This study uses complex spectral mapping to train the separation model.…”
Section: Evaluation Resultsmentioning
confidence: 99%